Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations5150
Missing cells1452
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 MiB
Average record size in memory1.4 KiB

Variable types

Text6
Categorical5
Numeric10

Alerts

PROPERTY_TYPE has constant value "flat"Constant
AREA is highly overall correlated with BEDROOM_NUM and 2 other fieldsHigh correlation
BEDROOM_NUM is highly overall correlated with AREA and 1 other fieldsHigh correlation
PRICE is highly overall correlated with AREA and 3 other fieldsHigh correlation
Price_per_sqft is highly overall correlated with AREA and 2 other fieldsHigh correlation
TOTAL_FLOOR is highly overall correlated with PRICE and 1 other fieldsHigh correlation
BALCONY_NUM is highly imbalanced (53.1%)Imbalance
SOCIETY_NAME has 80 (1.6%) missing valuesMissing
BALCONY_NUM has 472 (9.2%) missing valuesMissing
amenity_luxury has 662 (12.9%) missing valuesMissing
PROP_NAME has 80 (1.6%) missing valuesMissing
FORMATTED_LANDMARK_DETAILS has 158 (3.1%) missing valuesMissing
PROP_ID has unique valuesUnique
FACING has 1015 (19.7%) zerosZeros
FLOOR_NUM has 148 (2.9%) zerosZeros

Reproduction

Analysis started2024-08-22 19:56:35.101183
Analysis finished2024-08-22 19:56:54.908012
Duration19.81 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

PROP_ID
Text

UNIQUE 

Distinct5150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.1 KiB
2024-08-23T01:26:55.557675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters46350
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5150 ?
Unique (%)100.0%

Sample

1st rowJ71214794
2nd rowF70835394
3rd rowP69854924
4th rowE69854912
5th rowR69167152
ValueCountFrequency (%)
j71214794 1
 
< 0.1%
s70675172 1
 
< 0.1%
e69854912 1
 
< 0.1%
r69167152 1
 
< 0.1%
p69167148 1
 
< 0.1%
g70022436 1
 
< 0.1%
u69286152 1
 
< 0.1%
r69286148 1
 
< 0.1%
a68913472 1
 
< 0.1%
m69710576 1
 
< 0.1%
Other values (5140) 5140
99.8%
2024-08-23T01:26:56.173528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 6342
13.7%
0 5571
12.0%
6 4912
10.6%
1 4402
9.5%
2 3857
8.3%
8 3758
8.1%
4 3721
8.0%
9 3124
6.7%
3 2827
6.1%
5 2686
5.8%
Other values (26) 5150
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41200
88.9%
Uppercase Letter 5150
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 231
 
4.5%
C 230
 
4.5%
D 223
 
4.3%
O 211
 
4.1%
M 210
 
4.1%
X 209
 
4.1%
G 205
 
4.0%
Q 205
 
4.0%
V 203
 
3.9%
R 202
 
3.9%
Other values (16) 3021
58.7%
Decimal Number
ValueCountFrequency (%)
7 6342
15.4%
0 5571
13.5%
6 4912
11.9%
1 4402
10.7%
2 3857
9.4%
8 3758
9.1%
4 3721
9.0%
9 3124
7.6%
3 2827
6.9%
5 2686
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 41200
88.9%
Latin 5150
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 231
 
4.5%
C 230
 
4.5%
D 223
 
4.3%
O 211
 
4.1%
M 210
 
4.1%
X 209
 
4.1%
G 205
 
4.0%
Q 205
 
4.0%
V 203
 
3.9%
R 202
 
3.9%
Other values (16) 3021
58.7%
Common
ValueCountFrequency (%)
7 6342
15.4%
0 5571
13.5%
6 4912
11.9%
1 4402
10.7%
2 3857
9.4%
8 3758
9.1%
4 3721
9.0%
9 3124
7.6%
3 2827
6.9%
5 2686
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 6342
13.7%
0 5571
12.0%
6 4912
10.6%
1 4402
9.5%
2 3857
8.3%
8 3758
8.1%
4 3721
8.0%
9 3124
6.7%
3 2827
6.1%
5 2686
5.8%
Other values (26) 5150
11.1%

PROPERTY_TYPE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.9 KiB
flat
5150 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20600
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 5150
100.0%

Length

2024-08-23T01:26:56.390509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-23T01:26:56.529515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 5150
100.0%

Most occurring characters

ValueCountFrequency (%)
f 5150
25.0%
l 5150
25.0%
a 5150
25.0%
t 5150
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20600
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 5150
25.0%
l 5150
25.0%
a 5150
25.0%
t 5150
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 5150
25.0%
l 5150
25.0%
a 5150
25.0%
t 5150
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 5150
25.0%
l 5150
25.0%
a 5150
25.0%
t 5150
25.0%

SOCIETY_NAME
Text

MISSING 

Distinct1859
Distinct (%)36.7%
Missing80
Missing (%)1.6%
Memory size361.0 KiB
2024-08-23T01:26:56.854842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length15.37357
Min length2

Characters and Unicode

Total characters77944
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1250 ?
Unique (%)24.7%

Sample

1st rowBhawani Bandhan
2nd rowGanguly 4Sight Desire
3rd rowDtc Capital City
4th rowDtc Capital City
5th rowSai Sarovaar
ValueCountFrequency (%)
on 652
 
5.5%
request 616
 
5.2%
apartment 566
 
4.8%
city 233
 
2.0%
new 192
 
1.6%
the 170
 
1.4%
merlin 129
 
1.1%
booking 113
 
1.0%
siddha 112
 
0.9%
ps 109
 
0.9%
Other values (1771) 8996
75.7%
2024-08-23T01:26:57.455420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8119
 
10.4%
6838
 
8.8%
e 6758
 
8.7%
n 5282
 
6.8%
i 4591
 
5.9%
t 4575
 
5.9%
r 3891
 
5.0%
o 2851
 
3.7%
s 2621
 
3.4%
l 2343
 
3.0%
Other values (61) 30075
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58319
74.8%
Uppercase Letter 11739
 
15.1%
Space Separator 6838
 
8.8%
Decimal Number 636
 
0.8%
Other Punctuation 221
 
0.3%
Open Punctuation 77
 
0.1%
Close Punctuation 77
 
0.1%
Dash Punctuation 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8119
13.9%
e 6758
11.6%
n 5282
 
9.1%
i 4591
 
7.9%
t 4575
 
7.8%
r 3891
 
6.7%
o 2851
 
4.9%
s 2621
 
4.5%
l 2343
 
4.0%
u 2263
 
3.9%
Other values (16) 15025
25.8%
Uppercase Letter
ValueCountFrequency (%)
S 1560
13.3%
A 1462
12.5%
R 1300
11.1%
O 915
 
7.8%
P 697
 
5.9%
N 643
 
5.5%
C 631
 
5.4%
B 578
 
4.9%
M 563
 
4.8%
T 531
 
4.5%
Other values (16) 2859
24.4%
Decimal Number
ValueCountFrequency (%)
1 173
27.2%
2 151
23.7%
0 116
18.2%
6 37
 
5.8%
7 35
 
5.5%
5 32
 
5.0%
3 28
 
4.4%
9 25
 
3.9%
8 22
 
3.5%
4 17
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 109
49.3%
. 106
48.0%
/ 4
 
1.8%
' 1
 
0.5%
& 1
 
0.5%
Space Separator
ValueCountFrequency (%)
6838
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70058
89.9%
Common 7886
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8119
 
11.6%
e 6758
 
9.6%
n 5282
 
7.5%
i 4591
 
6.6%
t 4575
 
6.5%
r 3891
 
5.6%
o 2851
 
4.1%
s 2621
 
3.7%
l 2343
 
3.3%
u 2263
 
3.2%
Other values (42) 26764
38.2%
Common
ValueCountFrequency (%)
6838
86.7%
1 173
 
2.2%
2 151
 
1.9%
0 116
 
1.5%
, 109
 
1.4%
. 106
 
1.3%
( 77
 
1.0%
) 77
 
1.0%
- 37
 
0.5%
6 37
 
0.5%
Other values (9) 165
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8119
 
10.4%
6838
 
8.8%
e 6758
 
8.7%
n 5282
 
6.8%
i 4591
 
5.9%
t 4575
 
5.9%
r 3891
 
5.0%
o 2851
 
3.7%
s 2621
 
3.4%
l 2343
 
3.0%
Other values (61) 30075
38.6%

CITY
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size350.6 KiB
Kolkata South
2206 
Kolkata East
1593 
Kolkata North
1172 
Kolkata West
 
143
Kolkata Central
 
36

Length

Max length15
Median length13
Mean length12.676893
Min length12

Characters and Unicode

Total characters65286
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata North
2nd rowKolkata South
3rd rowKolkata East
4th rowKolkata East
5th rowKolkata East

Common Values

ValueCountFrequency (%)
Kolkata South 2206
42.8%
Kolkata East 1593
30.9%
Kolkata North 1172
22.8%
Kolkata West 143
 
2.8%
Kolkata Central 36
 
0.7%

Length

2024-08-23T01:26:57.685503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-23T01:26:57.868779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
kolkata 5150
50.0%
south 2206
21.4%
east 1593
 
15.5%
north 1172
 
11.4%
west 143
 
1.4%
central 36
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 11929
18.3%
t 10300
15.8%
o 8528
13.1%
l 5186
7.9%
K 5150
7.9%
k 5150
7.9%
5150
7.9%
h 3378
 
5.2%
u 2206
 
3.4%
S 2206
 
3.4%
Other values (8) 6103
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49836
76.3%
Uppercase Letter 10300
 
15.8%
Space Separator 5150
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11929
23.9%
t 10300
20.7%
o 8528
17.1%
l 5186
10.4%
k 5150
10.3%
h 3378
 
6.8%
u 2206
 
4.4%
s 1736
 
3.5%
r 1208
 
2.4%
e 179
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
K 5150
50.0%
S 2206
21.4%
E 1593
 
15.5%
N 1172
 
11.4%
W 143
 
1.4%
C 36
 
0.3%
Space Separator
ValueCountFrequency (%)
5150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60136
92.1%
Common 5150
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11929
19.8%
t 10300
17.1%
o 8528
14.2%
l 5186
8.6%
K 5150
8.6%
k 5150
8.6%
h 3378
 
5.6%
u 2206
 
3.7%
S 2206
 
3.7%
s 1736
 
2.9%
Other values (7) 4367
 
7.3%
Common
ValueCountFrequency (%)
5150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11929
18.3%
t 10300
15.8%
o 8528
13.1%
l 5186
7.9%
K 5150
7.9%
k 5150
7.9%
5150
7.9%
h 3378
 
5.2%
u 2206
 
3.4%
S 2206
 
3.4%
Other values (8) 6103
9.3%
Distinct443
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size334.7 KiB
2024-08-23T01:26:58.293765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length48
Median length42
Mean length9.5258252
Min length4

Characters and Unicode

Total characters49058
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)3.7%

Sample

1st rowMadhyamgram
2nd rowGaria
3rd rowRajarhat
4th rowRajarhat
5th rowNew Town
ValueCountFrequency (%)
new 858
 
10.7%
town 814
 
10.1%
rajarhat 366
 
4.5%
action 274
 
3.4%
area 274
 
3.4%
tollygunge 223
 
2.8%
bypass 188
 
2.3%
em 187
 
2.3%
road 180
 
2.2%
park 178
 
2.2%
Other values (478) 4514
56.0%
2024-08-23T01:26:59.043069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7880
16.1%
r 3902
 
8.0%
e 2948
 
6.0%
2906
 
5.9%
n 2715
 
5.5%
o 2610
 
5.3%
w 1784
 
3.6%
u 1727
 
3.5%
t 1638
 
3.3%
i 1552
 
3.2%
Other values (52) 19396
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37962
77.4%
Uppercase Letter 7846
 
16.0%
Space Separator 2906
 
5.9%
Decimal Number 317
 
0.6%
Other Punctuation 26
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7880
20.8%
r 3902
10.3%
e 2948
 
7.8%
n 2715
 
7.2%
o 2610
 
6.9%
w 1784
 
4.7%
u 1727
 
4.5%
t 1638
 
4.3%
i 1552
 
4.1%
l 1470
 
3.9%
Other values (16) 9736
25.6%
Uppercase Letter
ValueCountFrequency (%)
N 1283
16.4%
T 1153
14.7%
B 997
12.7%
A 758
9.7%
R 647
8.2%
S 467
 
6.0%
P 424
 
5.4%
K 385
 
4.9%
M 324
 
4.1%
D 270
 
3.4%
Other values (12) 1138
14.5%
Decimal Number
ValueCountFrequency (%)
1 121
38.2%
2 106
33.4%
3 69
21.8%
0 9
 
2.8%
5 5
 
1.6%
7 4
 
1.3%
9 1
 
0.3%
6 1
 
0.3%
4 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 10
38.5%
. 10
38.5%
/ 6
23.1%
Space Separator
ValueCountFrequency (%)
2906
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45808
93.4%
Common 3250
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7880
17.2%
r 3902
 
8.5%
e 2948
 
6.4%
n 2715
 
5.9%
o 2610
 
5.7%
w 1784
 
3.9%
u 1727
 
3.8%
t 1638
 
3.6%
i 1552
 
3.4%
l 1470
 
3.2%
Other values (38) 17582
38.4%
Common
ValueCountFrequency (%)
2906
89.4%
1 121
 
3.7%
2 106
 
3.3%
3 69
 
2.1%
, 10
 
0.3%
. 10
 
0.3%
0 9
 
0.3%
/ 6
 
0.2%
5 5
 
0.2%
7 4
 
0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7880
16.1%
r 3902
 
8.0%
e 2948
 
6.0%
2906
 
5.9%
n 2715
 
5.5%
o 2610
 
5.3%
w 1784
 
3.6%
u 1727
 
3.5%
t 1638
 
3.3%
i 1552
 
3.2%
Other values (52) 19396
39.5%

BEDROOM_NUM
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6231068
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:26:59.273967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76425411
Coefficient of variation (CV)0.29135455
Kurtosis1.6129823
Mean2.6231068
Median Absolute Deviation (MAD)1
Skewness0.52802839
Sum13509
Variance0.58408434
MonotonicityNot monotonic
2024-08-23T01:26:59.451867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 2305
44.8%
2 2111
41.0%
4 464
 
9.0%
1 212
 
4.1%
5 48
 
0.9%
6 8
 
0.2%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 212
 
4.1%
2 2111
41.0%
3 2305
44.8%
4 464
 
9.0%
5 48
 
0.9%
6 8
 
0.2%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 1
 
< 0.1%
6 8
 
0.2%
5 48
 
0.9%
4 464
 
9.0%
3 2305
44.8%
2 2111
41.0%
1 212
 
4.1%

BALCONY_NUM
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)0.1%
Missing472
Missing (%)9.2%
Memory size303.7 KiB
1.0
3558 
2.0
810 
0.0
 
150
3.0
 
125
4.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14034
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3558
69.1%
2.0 810
 
15.7%
0.0 150
 
2.9%
3.0 125
 
2.4%
4.0 35
 
0.7%
(Missing) 472
 
9.2%

Length

2024-08-23T01:26:59.725562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-23T01:26:59.894563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3558
76.1%
2.0 810
 
17.3%
0.0 150
 
3.2%
3.0 125
 
2.7%
4.0 35
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4828
34.4%
. 4678
33.3%
1 3558
25.4%
2 810
 
5.8%
3 125
 
0.9%
4 35
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9356
66.7%
Other Punctuation 4678
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4828
51.6%
1 3558
38.0%
2 810
 
8.7%
3 125
 
1.3%
4 35
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 4678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14034
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4828
34.4%
. 4678
33.3%
1 3558
25.4%
2 810
 
5.8%
3 125
 
0.9%
4 35
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4828
34.4%
. 4678
33.3%
1 3558
25.4%
2 810
 
5.8%
3 125
 
0.9%
4 35
 
0.2%

AREA
Real number (ℝ)

HIGH CORRELATION 

Distinct1313
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1231.087
Minimum320
Maximum6694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:00.110579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum320
5-th percentile600
Q1851.25
median1090
Q31422
95-th percentile2385
Maximum6694
Range6374
Interquartile range (IQR)570.75

Descriptive statistics

Standard deviation612.87673
Coefficient of variation (CV)0.49783381
Kurtosis10.101021
Mean1231.087
Median Absolute Deviation (MAD)261
Skewness2.4840413
Sum6340098
Variance375617.89
MonotonicityNot monotonic
2024-08-23T01:27:00.329688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1250 90
 
1.7%
1000 74
 
1.4%
900 71
 
1.4%
850 68
 
1.3%
800 64
 
1.2%
1100 59
 
1.1%
1150 55
 
1.1%
750 53
 
1.0%
950 49
 
1.0%
1200 42
 
0.8%
Other values (1303) 4525
87.9%
ValueCountFrequency (%)
320 4
 
0.1%
325 2
 
< 0.1%
345 1
 
< 0.1%
350 2
 
< 0.1%
360 2
 
< 0.1%
365 1
 
< 0.1%
370 1
 
< 0.1%
390 6
0.1%
391 2
 
< 0.1%
400 14
0.3%
ValueCountFrequency (%)
6694 1
< 0.1%
6000 1
< 0.1%
5915 1
< 0.1%
5889 1
< 0.1%
5572 1
< 0.1%
5554 1
< 0.1%
5350 1
< 0.1%
5070 1
< 0.1%
5069 1
< 0.1%
5000 1
< 0.1%

Price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2806
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6262.4177
Minimum2000
Maximum30063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:00.547373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2848.45
Q13959.25
median5300
Q37476.5
95-th percentile13120.75
Maximum30063
Range28063
Interquartile range (IQR)3517.25

Descriptive statistics

Standard deviation3404.5891
Coefficient of variation (CV)0.54365411
Kurtosis5.238298
Mean6262.4177
Median Absolute Deviation (MAD)1598.5
Skewness1.915474
Sum32251451
Variance11591227
MonotonicityNot monotonic
2024-08-23T01:27:00.779082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000 89
 
1.7%
4500 77
 
1.5%
3500 65
 
1.3%
4200 58
 
1.1%
5000 57
 
1.1%
3000 48
 
0.9%
3800 46
 
0.9%
6000 42
 
0.8%
5500 37
 
0.7%
3400 36
 
0.7%
Other values (2796) 4595
89.2%
ValueCountFrequency (%)
2000 6
0.1%
2019 1
 
< 0.1%
2046 1
 
< 0.1%
2047 1
 
< 0.1%
2067 2
 
< 0.1%
2072 1
 
< 0.1%
2074 1
 
< 0.1%
2078 1
 
< 0.1%
2100 4
0.1%
2130 1
 
< 0.1%
ValueCountFrequency (%)
30063 1
< 0.1%
29222 1
< 0.1%
28710 1
< 0.1%
28683 1
< 0.1%
26763 1
< 0.1%
26060 1
< 0.1%
24734 1
< 0.1%
24461 1
< 0.1%
24193 1
< 0.1%
23880 1
< 0.1%

PRICE
Real number (ℝ)

HIGH CORRELATION 

Distinct306
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.868
Minimum0.06
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:01.031080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.2
Q10.35
median0.58
Q30.95
95-th percentile2.65
Maximum10
Range9.94
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.96074593
Coefficient of variation (CV)1.1068502
Kurtosis19.412062
Mean0.868
Median Absolute Deviation (MAD)0.27
Skewness3.7245214
Sum4470.2
Variance0.92303274
MonotonicityNot monotonic
2024-08-23T01:27:01.280097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 129
 
2.5%
0.28 116
 
2.3%
0.65 109
 
2.1%
0.75 106
 
2.1%
0.45 100
 
1.9%
0.3 98
 
1.9%
0.35 93
 
1.8%
0.4 92
 
1.8%
0.42 83
 
1.6%
0.26 81
 
1.6%
Other values (296) 4143
80.4%
ValueCountFrequency (%)
0.06 1
 
< 0.1%
0.08 5
 
0.1%
0.09 5
 
0.1%
0.1 2
 
< 0.1%
0.11 6
 
0.1%
0.12 12
 
0.2%
0.13 7
 
0.1%
0.14 22
0.4%
0.15 16
 
0.3%
0.16 42
0.8%
ValueCountFrequency (%)
10 2
< 0.1%
9.5 1
 
< 0.1%
9 3
0.1%
8.25 1
 
< 0.1%
8.2 1
 
< 0.1%
8.12 1
 
< 0.1%
8 2
< 0.1%
7.99 1
 
< 0.1%
7.76 1
 
< 0.1%
7.7 1
 
< 0.1%

AGE
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size371.4 KiB
Relatively New Property
2182 
Old Property
1926 
New Property
650 
Moderately Old
392 

Length

Max length23
Median length12
Mean length16.812816
Min length12

Characters and Unicode

Total characters86586
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOld Property
2nd rowOld Property
3rd rowOld Property
4th rowOld Property
5th rowOld Property

Common Values

ValueCountFrequency (%)
Relatively New Property 2182
42.4%
Old Property 1926
37.4%
New Property 650
 
12.6%
Moderately Old 392
 
7.6%

Length

2024-08-23T01:27:01.501081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-23T01:27:01.677879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
property 4758
38.1%
new 2832
22.7%
old 2318
18.6%
relatively 2182
17.5%
moderately 392
 
3.1%

Most occurring characters

ValueCountFrequency (%)
e 12738
14.7%
r 9908
11.4%
t 7332
8.5%
y 7332
8.5%
7332
8.5%
l 7074
8.2%
o 5150
 
5.9%
p 4758
 
5.5%
P 4758
 
5.5%
N 2832
 
3.3%
Other values (8) 17372
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66772
77.1%
Uppercase Letter 12482
 
14.4%
Space Separator 7332
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12738
19.1%
r 9908
14.8%
t 7332
11.0%
y 7332
11.0%
l 7074
10.6%
o 5150
7.7%
p 4758
 
7.1%
w 2832
 
4.2%
d 2710
 
4.1%
a 2574
 
3.9%
Other values (2) 4364
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
P 4758
38.1%
N 2832
22.7%
O 2318
18.6%
R 2182
17.5%
M 392
 
3.1%
Space Separator
ValueCountFrequency (%)
7332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79254
91.5%
Common 7332
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12738
16.1%
r 9908
12.5%
t 7332
9.3%
y 7332
9.3%
l 7074
8.9%
o 5150
 
6.5%
p 4758
 
6.0%
P 4758
 
6.0%
N 2832
 
3.6%
w 2832
 
3.6%
Other values (7) 14540
18.3%
Common
ValueCountFrequency (%)
7332
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12738
14.7%
r 9908
11.4%
t 7332
8.5%
y 7332
8.5%
7332
8.5%
l 7074
8.2%
o 5150
 
5.9%
p 4758
 
5.5%
P 4758
 
5.5%
N 2832
 
3.3%
Other values (8) 17372
20.1%

FACING
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8930097
Minimum0
Maximum8
Zeros1015
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:01.851878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8563745
Coefficient of variation (CV)0.73371882
Kurtosis-1.5162592
Mean3.8930097
Median Absolute Deviation (MAD)3
Skewness-0.016079146
Sum20049
Variance8.1588752
MonotonicityNot monotonic
2024-08-23T01:27:02.032867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 1226
23.8%
0 1015
19.7%
2 840
16.3%
5 499
9.7%
3 462
 
9.0%
8 442
 
8.6%
1 266
 
5.2%
6 252
 
4.9%
4 148
 
2.9%
ValueCountFrequency (%)
0 1015
19.7%
1 266
 
5.2%
2 840
16.3%
3 462
 
9.0%
4 148
 
2.9%
5 499
9.7%
6 252
 
4.9%
7 1226
23.8%
8 442
 
8.6%
ValueCountFrequency (%)
8 442
 
8.6%
7 1226
23.8%
6 252
 
4.9%
5 499
9.7%
4 148
 
2.9%
3 462
 
9.0%
2 840
16.3%
1 266
 
5.2%
0 1015
19.7%

FURNISH
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size359.8 KiB
Fully furnished
3679 
Unfurnished
698 
Luxury furnished
533 
Semi-furnished
 
240

Length

Max length16
Median length15
Mean length14.514757
Min length11

Characters and Unicode

Total characters74751
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnfurnished
2nd rowUnfurnished
3rd rowUnfurnished
4th rowUnfurnished
5th rowUnfurnished

Common Values

ValueCountFrequency (%)
Fully furnished 3679
71.4%
Unfurnished 698
 
13.6%
Luxury furnished 533
 
10.3%
Semi-furnished 240
 
4.7%

Length

2024-08-23T01:27:02.237391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-23T01:27:02.405393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
furnished 4212
45.0%
fully 3679
39.3%
unfurnished 698
 
7.5%
luxury 533
 
5.7%
semi-furnished 240
 
2.6%

Most occurring characters

ValueCountFrequency (%)
u 9895
13.2%
l 7358
9.8%
n 5848
7.8%
r 5683
 
7.6%
i 5390
 
7.2%
e 5390
 
7.2%
s 5150
 
6.9%
d 5150
 
6.9%
f 5150
 
6.9%
h 5150
 
6.9%
Other values (9) 14587
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65149
87.2%
Uppercase Letter 5150
 
6.9%
Space Separator 4212
 
5.6%
Dash Punctuation 240
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 9895
15.2%
l 7358
11.3%
n 5848
9.0%
r 5683
8.7%
i 5390
8.3%
e 5390
8.3%
s 5150
7.9%
d 5150
7.9%
f 5150
7.9%
h 5150
7.9%
Other values (3) 4985
7.7%
Uppercase Letter
ValueCountFrequency (%)
F 3679
71.4%
U 698
 
13.6%
L 533
 
10.3%
S 240
 
4.7%
Space Separator
ValueCountFrequency (%)
4212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70299
94.0%
Common 4452
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 9895
14.1%
l 7358
10.5%
n 5848
8.3%
r 5683
8.1%
i 5390
7.7%
e 5390
7.7%
s 5150
7.3%
d 5150
7.3%
f 5150
7.3%
h 5150
7.3%
Other values (7) 10135
14.4%
Common
ValueCountFrequency (%)
4212
94.6%
- 240
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 9895
13.2%
l 7358
9.8%
n 5848
7.8%
r 5683
 
7.6%
i 5390
 
7.2%
e 5390
 
7.2%
s 5150
 
6.9%
d 5150
 
6.9%
f 5150
 
6.9%
h 5150
 
6.9%
Other values (9) 14587
19.5%

amenity_luxury
Real number (ℝ)

MISSING 

Distinct821
Distinct (%)18.3%
Missing662
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean331.91199
Minimum1
Maximum1105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:02.607366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1149
median291
Q3469.25
95-th percentile764
Maximum1105
Range1104
Interquartile range (IQR)320.25

Descriptive statistics

Standard deviation223.66267
Coefficient of variation (CV)0.67386137
Kurtosis0.26273066
Mean331.91199
Median Absolute Deviation (MAD)155.5
Skewness0.82166557
Sum1489621
Variance50024.988
MonotonicityNot monotonic
2024-08-23T01:27:02.843652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246 123
 
2.4%
338 119
 
2.3%
135 82
 
1.6%
68 69
 
1.3%
21 66
 
1.3%
103 45
 
0.9%
555 45
 
0.9%
441 40
 
0.8%
148 40
 
0.8%
237 34
 
0.7%
Other values (811) 3825
74.3%
(Missing) 662
 
12.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
5 5
 
0.1%
6 4
 
0.1%
9 1
 
< 0.1%
17 3
 
0.1%
19 3
 
0.1%
21 66
1.3%
22 1
 
< 0.1%
23 10
 
0.2%
24 25
 
0.5%
ValueCountFrequency (%)
1105 6
0.1%
1079 1
 
< 0.1%
1076 2
 
< 0.1%
1059 2
 
< 0.1%
1034 4
0.1%
1028 1
 
< 0.1%
1022 1
 
< 0.1%
1020 1
 
< 0.1%
1014 1
 
< 0.1%
1007 1
 
< 0.1%

FLOOR_NUM
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3452427
Minimum0
Maximum34
Zeros148
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:03.048015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile15
Maximum34
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.6492044
Coefficient of variation (CV)1.0699528
Kurtosis6.1742319
Mean4.3452427
Median Absolute Deviation (MAD)2
Skewness2.2754367
Sum22378
Variance21.615102
MonotonicityNot monotonic
2024-08-23T01:27:03.256555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1236
24.0%
2 965
18.7%
3 846
16.4%
4 575
11.2%
5 222
 
4.3%
8 148
 
2.9%
0 148
 
2.9%
10 139
 
2.7%
7 138
 
2.7%
6 117
 
2.3%
Other values (23) 616
12.0%
ValueCountFrequency (%)
0 148
 
2.9%
1 1236
24.0%
2 965
18.7%
3 846
16.4%
4 575
11.2%
5 222
 
4.3%
6 117
 
2.3%
7 138
 
2.7%
8 148
 
2.9%
9 99
 
1.9%
ValueCountFrequency (%)
34 3
 
0.1%
33 2
 
< 0.1%
31 2
 
< 0.1%
30 3
 
0.1%
28 6
0.1%
27 4
0.1%
26 7
0.1%
25 8
0.2%
24 4
0.1%
23 5
0.1%

LATITUDE
Real number (ℝ)

Distinct2095
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.158748
Minimum0
Maximum88.491741
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:03.482809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.440536
Q122.496281
median22.570171
Q322.62265
95-th percentile22.731442
Maximum88.491741
Range88.491741
Interquartile range (IQR)0.12636845

Descriptive statistics

Standard deviation10.401722
Coefficient of variation (CV)0.43055716
Kurtosis33.767246
Mean24.158748
Median Absolute Deviation (MAD)0.06397705
Skewness5.9012933
Sum124417.55
Variance108.19582
MonotonicityNot monotonic
2024-08-23T01:27:03.700796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.6006912 244
 
4.7%
22.5 109
 
2.1%
88.35469 89
 
1.7%
22.4658051 44
 
0.9%
22.730184 38
 
0.7%
22.44390298 38
 
0.7%
22.48234 35
 
0.7%
22.69924 35
 
0.7%
22.581047 35
 
0.7%
22.548114 34
 
0.7%
Other values (2085) 4449
86.4%
ValueCountFrequency (%)
0 14
0.3%
13.036358 1
 
< 0.1%
13.06146 1
 
< 0.1%
20.60288 4
 
0.1%
21.623056 2
 
< 0.1%
22.28355 1
 
< 0.1%
22.3269857 1
 
< 0.1%
22.361462 2
 
< 0.1%
22.364447 1
 
< 0.1%
22.364731 4
 
0.1%
ValueCountFrequency (%)
88.491741 1
 
< 0.1%
88.4616753 1
 
< 0.1%
88.450763 9
 
0.2%
88.378133 20
 
0.4%
88.36087 3
 
0.1%
88.35469 89
1.7%
88.323499 1
 
< 0.1%
88.300239 1
 
< 0.1%
88.282673 2
 
< 0.1%
88.21613 1
 
< 0.1%

LONGITUDE
Real number (ℝ)

Distinct2074
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.500507
Minimum0
Maximum88.609395
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:03.928812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile88.296391
Q188.366378
median88.402079
Q388.455814
95-th percentile88.496023
Maximum88.609395
Range88.609395
Interquartile range (IQR)0.089436

Descriptive statistics

Standard deviation11.272944
Coefficient of variation (CV)0.13032229
Kurtosis32.57726
Mean86.500507
Median Absolute Deviation (MAD)0.046749
Skewness-5.8303996
Sum445477.61
Variance127.07927
MonotonicityNot monotonic
2024-08-23T01:27:04.133815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.4694535 244
 
4.7%
88.35 109
 
2.1%
22.536025 89
 
1.7%
88.384625 52
 
1.0%
88.29787361 38
 
0.7%
88.455814 38
 
0.7%
88.462231 35
 
0.7%
88.38115 35
 
0.7%
88.41252 35
 
0.7%
88.4004968 34
 
0.7%
Other values (2064) 4441
86.2%
ValueCountFrequency (%)
0 14
 
0.3%
22.48357 1
 
< 0.1%
22.493402 2
 
< 0.1%
22.5103 1
 
< 0.1%
22.511184 1
 
< 0.1%
22.536025 89
1.7%
22.58802 2
 
< 0.1%
22.59436 3
 
0.1%
22.62961 1
 
< 0.1%
22.632687 9
 
0.2%
ValueCountFrequency (%)
88.609395 1
 
< 0.1%
88.5255283 3
0.1%
88.525052 1
 
< 0.1%
88.524436 1
 
< 0.1%
88.52036476 1
 
< 0.1%
88.51997 2
< 0.1%
88.5199079 1
 
< 0.1%
88.51958012 2
< 0.1%
88.5193094 1
 
< 0.1%
88.51896 1
 
< 0.1%

PROP_NAME
Text

MISSING 

Distinct2011
Distinct (%)39.7%
Missing80
Missing (%)1.6%
Memory size361.3 KiB
2024-08-23T01:27:04.691509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length15.438264
Min length2

Characters and Unicode

Total characters78272
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1384 ?
Unique (%)27.3%

Sample

1st rowBhawani Bandhan
2nd rowGanguly 4Sight Desire
3rd rowDTC Capital City
4th rowDTC Capital City
5th rowSai Sarovaar
ValueCountFrequency (%)
on 652
 
5.5%
request 616
 
5.2%
apartment 566
 
4.8%
city 233
 
2.0%
new 192
 
1.6%
the 170
 
1.4%
merlin 129
 
1.1%
booking 113
 
1.0%
siddha 112
 
0.9%
ps 109
 
0.9%
Other values (1771) 8996
75.7%
2024-08-23T01:27:05.445513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7918
 
10.1%
7166
 
9.2%
e 6456
 
8.2%
n 5080
 
6.5%
t 4343
 
5.5%
i 4288
 
5.5%
r 3987
 
5.1%
o 2980
 
3.8%
s 2475
 
3.2%
l 2202
 
2.8%
Other values (61) 31377
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56202
71.8%
Uppercase Letter 13856
 
17.7%
Space Separator 7166
 
9.2%
Decimal Number 636
 
0.8%
Other Punctuation 221
 
0.3%
Open Punctuation 77
 
0.1%
Close Punctuation 77
 
0.1%
Dash Punctuation 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7918
14.1%
e 6456
11.5%
n 5080
 
9.0%
t 4343
 
7.7%
i 4288
 
7.6%
r 3987
 
7.1%
o 2980
 
5.3%
s 2475
 
4.4%
l 2202
 
3.9%
u 2176
 
3.9%
Other values (16) 14297
25.4%
Uppercase Letter
ValueCountFrequency (%)
S 1706
12.3%
A 1663
12.0%
R 1204
 
8.7%
N 845
 
6.1%
P 788
 
5.7%
O 786
 
5.7%
T 763
 
5.5%
C 734
 
5.3%
E 694
 
5.0%
M 649
 
4.7%
Other values (16) 4024
29.0%
Decimal Number
ValueCountFrequency (%)
1 173
27.2%
2 151
23.7%
0 116
18.2%
6 37
 
5.8%
7 35
 
5.5%
5 32
 
5.0%
3 28
 
4.4%
9 25
 
3.9%
8 22
 
3.5%
4 17
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 109
49.3%
. 106
48.0%
/ 4
 
1.8%
' 1
 
0.5%
& 1
 
0.5%
Space Separator
ValueCountFrequency (%)
7166
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70058
89.5%
Common 8214
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7918
 
11.3%
e 6456
 
9.2%
n 5080
 
7.3%
t 4343
 
6.2%
i 4288
 
6.1%
r 3987
 
5.7%
o 2980
 
4.3%
s 2475
 
3.5%
l 2202
 
3.1%
u 2176
 
3.1%
Other values (42) 28153
40.2%
Common
ValueCountFrequency (%)
7166
87.2%
1 173
 
2.1%
2 151
 
1.8%
0 116
 
1.4%
, 109
 
1.3%
. 106
 
1.3%
( 77
 
0.9%
) 77
 
0.9%
- 37
 
0.5%
6 37
 
0.5%
Other values (9) 165
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7918
 
10.1%
7166
 
9.2%
e 6456
 
8.2%
n 5080
 
6.5%
t 4343
 
5.5%
i 4288
 
5.5%
r 3987
 
5.1%
o 2980
 
3.8%
s 2475
 
3.2%
l 2202
 
2.8%
Other values (61) 31377
40.1%

TOTAL_FLOOR
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.484466
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2024-08-23T01:27:05.676155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q314
95-th percentile26
Maximum42
Range41
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.0515703
Coefficient of variation (CV)0.84892183
Kurtosis0.99434673
Mean9.484466
Median Absolute Deviation (MAD)2
Skewness1.3667346
Sum48845
Variance64.827784
MonotonicityNot monotonic
2024-08-23T01:27:05.894154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4 1664
32.3%
5 591
 
11.5%
3 577
 
11.2%
12 195
 
3.8%
20 175
 
3.4%
6 148
 
2.9%
11 144
 
2.8%
14 140
 
2.7%
13 129
 
2.5%
7 129
 
2.5%
Other values (29) 1258
24.4%
ValueCountFrequency (%)
1 10
 
0.2%
2 70
 
1.4%
3 577
 
11.2%
4 1664
32.3%
5 591
 
11.5%
6 148
 
2.9%
7 129
 
2.5%
8 48
 
0.9%
9 44
 
0.9%
10 76
 
1.5%
ValueCountFrequency (%)
42 3
 
0.1%
38 4
 
0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
35 45
0.9%
34 11
 
0.2%
33 34
0.7%
32 14
 
0.3%
31 23
0.4%
30 28
0.5%
Distinct1152
Distinct (%)23.1%
Missing158
Missing (%)3.1%
Memory size749.3 KiB
2024-08-23T01:27:06.143175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length198
Median length151
Mean length95.659856
Min length2

Characters and Unicode

Total characters477534
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)9.8%

Sample

1st row['5 Religious Places', '15 Hospitals', '1 Attraction', '1 Bus Depot']
2nd row['3 Metro Stations', '9 Religious Places', '3 ATMs', '15 Hospitals', '1 Attraction', '2 Pharmacys', '2 Miscellaneouss']
3rd row['1 Shopping', '1 Education', '1 Hospital', '1 Airport', '1 Park', '1 Office Complex']
4th row['1 Shopping', '1 Education', '1 Hospital', '1 Airport', '1 Park', '1 Office Complex']
5th row['1 Metro Station', '1 Religious Place', '1 ATM', '6 Hospitals', '1 Pharmacy', '4 Office Complexes', '1 Parking', '3 Bus Depots']
ValueCountFrequency (%)
1 14580
21.3%
2 5815
 
8.5%
hospitals 3926
 
5.7%
religious 3180
 
4.6%
atms 2467
 
3.6%
3 2466
 
3.6%
bus 2368
 
3.5%
places 2286
 
3.3%
miscellaneous 2129
 
3.1%
metro 1917
 
2.8%
Other values (71) 27426
40.0%
2024-08-23T01:27:06.684098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63568
 
13.3%
' 58714
 
12.3%
s 30995
 
6.5%
i 26168
 
5.5%
o 24901
 
5.2%
, 24389
 
5.1%
a 22122
 
4.6%
t 21737
 
4.6%
e 20797
 
4.4%
l 18839
 
3.9%
Other values (42) 165304
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 243644
51.0%
Other Punctuation 83103
 
17.4%
Space Separator 63568
 
13.3%
Uppercase Letter 45031
 
9.4%
Decimal Number 32204
 
6.7%
Close Punctuation 4992
 
1.0%
Open Punctuation 4992
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 30995
12.7%
i 26168
10.7%
o 24901
10.2%
a 22122
9.1%
t 21737
8.9%
e 20797
8.5%
l 18839
7.7%
p 12655
 
5.2%
c 11804
 
4.8%
n 11411
 
4.7%
Other values (13) 42215
17.3%
Uppercase Letter
ValueCountFrequency (%)
M 7378
16.4%
P 5629
12.5%
H 5113
11.4%
A 4974
11.0%
S 4582
10.2%
R 3955
8.8%
T 2926
 
6.5%
B 2715
 
6.0%
C 2237
 
5.0%
D 2160
 
4.8%
Other values (4) 3362
7.5%
Decimal Number
ValueCountFrequency (%)
1 17017
52.8%
2 6818
21.2%
3 2832
 
8.8%
4 1579
 
4.9%
5 1133
 
3.5%
6 783
 
2.4%
7 750
 
2.3%
0 525
 
1.6%
8 467
 
1.5%
9 300
 
0.9%
Other Punctuation
ValueCountFrequency (%)
' 58714
70.7%
, 24389
29.3%
Space Separator
ValueCountFrequency (%)
63568
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4992
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 288675
60.5%
Common 188859
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 30995
 
10.7%
i 26168
 
9.1%
o 24901
 
8.6%
a 22122
 
7.7%
t 21737
 
7.5%
e 20797
 
7.2%
l 18839
 
6.5%
p 12655
 
4.4%
c 11804
 
4.1%
n 11411
 
4.0%
Other values (27) 87246
30.2%
Common
ValueCountFrequency (%)
63568
33.7%
' 58714
31.1%
, 24389
 
12.9%
1 17017
 
9.0%
2 6818
 
3.6%
] 4992
 
2.6%
[ 4992
 
2.6%
3 2832
 
1.5%
4 1579
 
0.8%
5 1133
 
0.6%
Other values (5) 2825
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63568
 
13.3%
' 58714
 
12.3%
s 30995
 
6.5%
i 26168
 
5.5%
o 24901
 
5.2%
, 24389
 
5.1%
a 22122
 
4.6%
t 21737
 
4.6%
e 20797
 
4.4%
l 18839
 
3.9%
Other values (42) 165304
34.6%
Distinct4948
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2024-08-23T01:27:07.394612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3693
Median length981
Mean length562.06252
Min length31

Characters and Unicode

Total characters2894622
Distinct characters92
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4841 ?
Unique (%)94.0%

Sample

1st rowExperience a new style of living with Bhawani Bandhan. It offers an exclusive range of 2 BHK apartments in Madhyamgram, Kolkata North. Here is a steal deal for you. Book your 2 BHK apartment here at a never before price of Rs. 39 Lac. The unit has a super built-up area of 920.0 sq. ft. This is an under construction project. It has been designed keeping every small to large needs of residents in consideration. Plus, a comprehensive range of amenities including Indoor Games, Card Room, Gymnasium, Lift(s), Banquet Hall, etc. make it one of the most desirable residential projects in Kolkata North.
2nd rowLet your dream of owning a flat come true with Ganguly 4Sight Desire. It offers an exclusive range of 1 BHK flats in Garia, Kolkata South. We bring you an amazing deal of a 1 BHK that is currently available at price of Rs. 27.29 Lac and has a super built-up area of 535.0 sq. ft. Ganguly 4Sight Desire is a secured gated community that further has 24x7 security systems. It has round the clock power back up as well as features many more attractive facilities with a host of amenities like Lift(s), Community Hall, Indoor Games, Gated Community, Car Parking, etc.
3rd rowBook your 4 BHK apartment in DTC Capital City, Rajarhat, Kolkata East. Having a super built-up area of 1940.0 sq. ft., the property is well ventilated and promises an exclusive view and refreshing vibes. The project is a superbly designed project set amidst excellent surroundings and offers residents a world-class infrastructure. Look at its range of general amenities that include Party Lawn, Reflexology Park, Amphitheatre, Library, Senior Citizen Sitout and the premium amenities, including Spa, Squash Court, Basketball Court and what not. Now, you can buy this exclusive 4 BHK apartment at Rs. 92 Lac.
4th rowMake DTC Capital City your next home. Book your 2 BHK flat in Rajarhat, Kolkata East. With a super built-up area of 910.0 sq. ft., the flat combines the finest design and amenities in Kolkata East to provide a living experience unlike any other. Here is an exclusive deal for you. Buy your 2 BHK flat for Rs. 42 Lac. It is a new launch property which is unique in its perfect harmony of classic form and modern construction. The features and the amenities like Banquet Hall, Cricket Pitch, Jogging Track, Cafeteria, Reflexology Park and many more make this project an epitome of modern living. Further, the society is well connected with all means of public transport.
5th rowBook a spectacular property in Sai Sarovaar that brings 3 BHK apartments in New Town, Kolkata East. We have a 3 BHK apartment with a super built-up area of 1163.0 sq. ft., available at an economical price of Rs. 54.66 Lac. A close attention has been paid to each detail for a comfortable living. There are 1 towers in Sai Sarovaar. Further, you can stay assured of Car Parking, Lift(s), etc. Loaded with all the modern amenities, the project is certainly a steal deal.
ValueCountFrequency (%)
the 22963
 
4.6%
is 19767
 
4.0%
of 14821
 
3.0%
a 13431
 
2.7%
this 11694
 
2.4%
and 11475
 
2.3%
flat 10744
 
2.2%
in 9696
 
2.0%
to 8390
 
1.7%
for 7988
 
1.6%
Other values (9138) 363558
73.5%
2024-08-23T01:27:08.175989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
481632
16.6%
e 220422
 
7.6%
a 212235
 
7.3%
t 195839
 
6.8%
o 186522
 
6.4%
i 169414
 
5.9%
s 160046
 
5.5%
r 142054
 
4.9%
n 128105
 
4.4%
l 119043
 
4.1%
Other values (82) 879310
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2201544
76.1%
Space Separator 481658
 
16.6%
Other Punctuation 73345
 
2.5%
Uppercase Letter 55534
 
1.9%
Decimal Number 52842
 
1.8%
Control 14825
 
0.5%
Dash Punctuation 6089
 
0.2%
Close Punctuation 4352
 
0.2%
Open Punctuation 3998
 
0.1%
Math Symbol 400
 
< 0.1%
Other values (3) 35
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 220422
 
10.0%
a 212235
 
9.6%
t 195839
 
8.9%
o 186522
 
8.5%
i 169414
 
7.7%
s 160046
 
7.3%
r 142054
 
6.5%
n 128105
 
5.8%
l 119043
 
5.4%
h 80193
 
3.6%
Other values (16) 587671
26.7%
Uppercase Letter
ValueCountFrequency (%)
T 17117
30.8%
F 5240
 
9.4%
A 4811
 
8.7%
C 3400
 
6.1%
S 2949
 
5.3%
B 2664
 
4.8%
I 2402
 
4.3%
L 2086
 
3.8%
N 1662
 
3.0%
P 1574
 
2.8%
Other values (16) 11629
20.9%
Other Punctuation
ValueCountFrequency (%)
. 41247
56.2%
, 24707
33.7%
/ 2853
 
3.9%
: 2411
 
3.3%
' 880
 
1.2%
& 447
 
0.6%
? 295
 
0.4%
* 250
 
0.3%
! 108
 
0.1%
% 102
 
0.1%
Other values (4) 45
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 11247
21.3%
2 11099
21.0%
0 6932
13.1%
3 6833
12.9%
5 4840
9.2%
4 4816
9.1%
7 2190
 
4.1%
8 1788
 
3.4%
6 1652
 
3.1%
9 1445
 
2.7%
Math Symbol
ValueCountFrequency (%)
+ 286
71.5%
| 77
 
19.2%
= 26
 
6.5%
~ 7
 
1.8%
> 3
 
0.8%
< 1
 
0.2%
Space Separator
ValueCountFrequency (%)
481632
> 99.9%
  26
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 6063
99.6%
– 26
 
0.4%
Control
ValueCountFrequency (%)
14825
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4352
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3998
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 30
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2257078
78.0%
Common 637544
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 220422
 
9.8%
a 212235
 
9.4%
t 195839
 
8.7%
o 186522
 
8.3%
i 169414
 
7.5%
s 160046
 
7.1%
r 142054
 
6.3%
n 128105
 
5.7%
l 119043
 
5.3%
h 80193
 
3.6%
Other values (42) 643205
28.5%
Common
ValueCountFrequency (%)
481632
75.5%
. 41247
 
6.5%
, 24707
 
3.9%
14825
 
2.3%
1 11247
 
1.8%
2 11099
 
1.7%
0 6932
 
1.1%
3 6833
 
1.1%
- 6063
 
1.0%
5 4840
 
0.8%
Other values (30) 28119
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2894540
> 99.9%
Punctuation 56
 
< 0.1%
None 26
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
481632
16.6%
e 220422
 
7.6%
a 212235
 
7.3%
t 195839
 
6.8%
o 186522
 
6.4%
i 169414
 
5.9%
s 160046
 
5.5%
r 142054
 
4.9%
n 128105
 
4.4%
l 119043
 
4.1%
Other values (79) 879228
30.4%
Punctuation
ValueCountFrequency (%)
’ 30
53.6%
– 26
46.4%
None
ValueCountFrequency (%)
  26
100.0%

Interactions

2024-08-23T01:26:52.302931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:37.417311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:39.122451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:40.787012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:42.446737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:44.030637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:45.924475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:47.531603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.174378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:50.742118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:52.475942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:37.622743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:39.312464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:40.977006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:42.615995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:44.199635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:46.105470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:47.725603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.337362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:50.926118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:52.636197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:37.790743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:39.492466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:41.136353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:42.784947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:44.385638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:46.255471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:47.887605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.496359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:51.079143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:52.786202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:37.955745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:39.652451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:41.307035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:42.933940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:44.559626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:46.416474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:48.051458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.657364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:51.243121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:52.943206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:38.113747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:39.825262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:41.466030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:43.083954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:44.720623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:46.586471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:48.216442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.816362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:51.397124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:53.095213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:38.302748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:39.988279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:41.644030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:43.259959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:44.892529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:46.754157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:48.397450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.974359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:51.562133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:53.247202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:38.473447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:40.144269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:41.805032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:43.413936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:45.282866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:46.915150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:48.557438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:50.110358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:51.705119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:53.414193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:38.641456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:40.322267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:41.973033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:43.586936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:45.454860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:47.081597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:48.729443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:50.264357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:51.874129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:53.568836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:38.799452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:40.496012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:42.117740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:43.738941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:45.615471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:47.234615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:48.872381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:50.461360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:52.007121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:53.705837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:38.963452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:40.641015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:42.277730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:43.878645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:45.775475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:47.378591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:49.023381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:50.611135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-23T01:26:52.147933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-23T01:27:08.334017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AGEAREABALCONY_NUMBEDROOM_NUMCITYFACINGFLOOR_NUMFURNISHLATITUDELONGITUDEPRICEPrice_per_sqftTOTAL_FLOORamenity_luxury
AGE1.0000.0580.0980.0830.1250.2110.0760.2770.0600.0710.0600.1100.1480.066
AREA0.0581.0000.2610.8220.118-0.0290.2950.078-0.0520.0590.8550.5570.4100.156
BALCONY_NUM0.0980.2611.0000.2490.1110.0390.1290.0620.0880.1020.1690.1190.1420.085
BEDROOM_NUM0.0830.8220.2491.0000.090-0.0510.1930.099-0.0230.0350.6930.4380.3160.095
CITY0.1250.1180.1110.0901.0000.1000.0920.0650.0560.0700.1520.2150.1710.131
FACING0.211-0.0290.039-0.0510.1001.0000.1820.337-0.022-0.030-0.100-0.126-0.1610.170
FLOOR_NUM0.0760.2950.1290.1930.0920.1821.0000.1210.0010.0660.3520.3250.4650.253
FURNISH0.2770.0780.0620.0990.0650.3370.1211.0000.0550.0590.0760.0950.1670.110
LATITUDE0.060-0.0520.088-0.0230.056-0.0220.0010.0551.0000.324-0.129-0.1740.0500.016
LONGITUDE0.0710.0590.1020.0350.070-0.0300.0660.0590.3241.0000.0320.0010.1730.067
PRICE0.0600.8550.1690.6930.152-0.1000.3520.076-0.1290.0321.0000.8870.5750.174
Price_per_sqft0.1100.5570.1190.4380.215-0.1260.3250.095-0.1740.0010.8871.0000.5790.155
TOTAL_FLOOR0.1480.4100.1420.3160.171-0.1610.4650.1670.0500.1730.5750.5791.0000.270
amenity_luxury0.0660.1560.0850.0950.1310.1700.2530.1100.0160.0670.1740.1550.2701.000

Missing values

2024-08-23T01:26:53.951840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-23T01:26:54.437916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-23T01:26:54.748378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PROP_IDPROPERTY_TYPESOCIETY_NAMECITYlocationBEDROOM_NUMBALCONY_NUMAREAPrice_per_sqftPRICEAGEFACINGFURNISHamenity_luxuryFLOOR_NUMLATITUDELONGITUDEPROP_NAMETOTAL_FLOORFORMATTED_LANDMARK_DETAILSDESCRIPTION
0J71214794flatBhawani BandhanKolkata NorthMadhyamgram2.0NaN9204239.00.39Old Property0UnfurnishedNaN122.69000388.459080Bhawani Bandhan11.0['5 Religious Places', '15 Hospitals', '1 Attraction', '1 Bus Depot']Experience a new style of living with Bhawani Bandhan. It offers an exclusive range of 2 BHK apartments in Madhyamgram, Kolkata North. Here is a steal deal for you. Book your 2 BHK apartment here at a never before price of Rs. 39 Lac. The unit has a super built-up area of 920.0 sq. ft. \n\nThis is an under construction project. It has been designed keeping every small to large needs of residents in consideration. Plus, a comprehensive range of amenities including Indoor Games, Card Room, Gymnasium, Lift(s), Banquet Hall, etc. make it one of the most desirable residential projects in Kolkata North.
1F70835394flatGanguly 4Sight DesireKolkata SouthGaria1.0NaN5355100.00.27Old Property0UnfurnishedNaN122.46878088.380720Ganguly 4Sight Desire7.0['3 Metro Stations', '9 Religious Places', '3 ATMs', '15 Hospitals', '1 Attraction', '2 Pharmacys', '2 Miscellaneouss']Let your dream of owning a flat come true with Ganguly 4Sight Desire. It offers an exclusive range of 1 BHK flats in Garia, Kolkata South. We bring you an amazing deal of a 1 BHK that is currently available at price of Rs. 27.29 Lac and has a super built-up area of 535.0 sq. ft. \n\nGanguly 4Sight Desire is a secured gated community that further has 24x7 security systems. It has round the clock power back up as well as features many more attractive facilities with a host of amenities like Lift(s), Community Hall, Indoor Games, Gated Community, Car Parking, etc.
2P69854924flatDtc Capital CityKolkata EastRajarhat4.0NaN19404742.00.92Old Property0UnfurnishedNaN122.56208788.505528DTC Capital City20.0['1 Shopping', '1 Education', '1 Hospital', '1 Airport', '1 Park', '1 Office Complex']Book your 4 BHK apartment in DTC Capital City, Rajarhat, Kolkata East. Having a super built-up area of 1940.0 sq. ft., the property is well ventilated and promises an exclusive view and refreshing vibes.\n\nThe project is a superbly designed project set amidst excellent surroundings and offers residents a world-class infrastructure. Look at its range of general amenities that include Party Lawn, Reflexology Park, Amphitheatre, Library, Senior Citizen Sitout and the premium amenities, including Spa, Squash Court, Basketball Court and what not. Now, you can buy this exclusive 4 BHK apartment at Rs. 92 Lac.
3E69854912flatDtc Capital CityKolkata EastRajarhat2.0NaN9104615.00.42Old Property0UnfurnishedNaN122.56208788.505528DTC Capital City20.0['1 Shopping', '1 Education', '1 Hospital', '1 Airport', '1 Park', '1 Office Complex']Make DTC Capital City your next home. Book your 2 BHK flat in Rajarhat, Kolkata East. With a super built-up area of 910.0 sq. ft., the flat combines the finest design and amenities in Kolkata East to provide a living experience unlike any other. Here is an exclusive deal for you. Buy your 2 BHK flat for Rs. 42 Lac. It is a new launch property which is unique in its perfect harmony of classic form and modern construction.\n\nThe features and the amenities like Banquet Hall, Cricket Pitch, Jogging Track, Cafeteria, Reflexology Park and many more make this project an epitome of modern living. Further, the society is well connected with all means of public transport.
4R69167152flatSai SarovaarKolkata EastNew Town3.0NaN11634700.00.55Old Property0UnfurnishedNaN122.57083088.483880Sai Sarovaar4.0['1 Metro Station', '1 Religious Place', '1 ATM', '6 Hospitals', '1 Pharmacy', '4 Office Complexes', '1 Parking', '3 Bus Depots']Book a spectacular property in Sai Sarovaar that brings 3 BHK apartments in New Town, Kolkata East. We have a 3 BHK apartment with a super built-up area of 1163.0 sq. ft., available at an economical price of Rs. 54.66 Lac.\n\nA close attention has been paid to each detail for a comfortable living. There are 1 towers in Sai Sarovaar. Further, you can stay assured of Car Parking, Lift(s), etc. Loaded with all the modern amenities, the project is certainly a steal deal.
5P69167148flatSai SarovaarKolkata EastNew Town2.0NaN9014700.00.42Old Property0UnfurnishedNaN122.57083088.483880Sai Sarovaar4.0['1 Metro Station', '1 Religious Place', '1 ATM', '6 Hospitals', '1 Pharmacy', '4 Office Complexes', '1 Parking', '3 Bus Depots']Sai Sarovaar is a residential project, offering a range of 2 BHK flats in New Town, Kolkata East. It hosts exclusively designed 1 towers, each presenting an epitome of class and simplicity. We bring you a chance to book your 2 BHK flat that has a super built-up area of 901.0 sq. ft. \n\nIt is an under construction property. With an impressive layout and a comprehensive range of amenities like Car Parking, Lift(s), etc., the project leaves no stone unturned to amaze.
6G70022436flatMerlin SereniaKolkata NorthBt Road2.0NaN7328759.00.64Old Property0UnfurnishedNaN122.64664088.378170Merlin Serenia25.0['2 Connectivities', '1 Education']Book a spectacular property in Merlin Serenia that brings 2 BHK apartments in BT Road, Kolkata North. We have a 2 BHK apartment with a built-up area of 732.0 sq. ft., available at an economical price of Rs. 64.12 Lac.\n\nA close attention has been paid to each detail for a comfortable living. There are 2 towers in Merlin Serenia. Further, you can stay assured of Banquet Hall, Steam Room, Rain Water Harvesting, Spa, Gymnasium, etc. Loaded with all the modern amenities, the project is certainly a steal deal.
7U69286152flatSunday On The HouseKolkata EastNew Town3.0NaN14605200.00.76Old Property0UnfurnishedNaN122.60336188.447509Sunday On The House5.0['6 Connectivities', '2 Educations', '1 Hospital', '1 Airport', '1 Office Complex']Sunday On The House is a ready to move project, offering a great 3 BHK flat in New Town, Kolkata East. The unit has a super built-up area of 1460.0 sq. ft. and is available at a price of Rs. 75.92 Lac. \n\nSunday On The House is well connected to the city areas and features a large number of amenities to fit your needs. It is equipped with highlights such as InterCom, Senior Citizen Sitout, Landscape Garden, Fire Fighting Systems, Library, etc. that make it one of the most sought after neighborhoods.
8R69286148flatSunday On The HouseKolkata EastNew Town2.0NaN10505200.00.55Old Property0UnfurnishedNaN122.60336188.447509Sunday On The House5.0['6 Connectivities', '2 Educations', '1 Hospital', '1 Airport', '1 Office Complex']Experience a new style of living with Sunday On The House. It offers an exclusive range of 2 BHK apartments in New Town, Kolkata East. Here is a steal deal for you. Book your 2 BHK apartment here at a never before price of Rs. 54.6 Lac. The unit has a super built-up area of 1050.0 sq. ft. \n\nThis is a ready to move project. It has been designed keeping every small to large needs of residents in consideration. Plus, a comprehensive range of amenities including Yoga/Meditation Area, Gated Community, Landscape Garden, InterCom, Car Parking, etc. make it one of the most desirable residential projects in Kolkata East.
9M69710576flatSpotlight CountrysideKolkata SouthRajpur3.0NaN10843500.00.38Old Property0UnfurnishedNaN122.41628388.402759Spotlight Countryside11.0['1 Shopping', '1 Connectivity', '2 Educations', '1 Hospital', '1 Airport', '1 Railway Station', '1 Hotels']Spotlight Countryside is a new launch project, offering a great 3 BHK flat in Narendrapur, Kolkata South. The unit has a super built-up area of 1084.0 sq. ft. and is available at a price of Rs. 37.94 Lac. \n\nSpotlight Countryside is well connected to the city areas and features a large number of amenities to fit your needs. It is equipped with highlights such as Car Parking, Paved Compound, Community Hall, Internal Street Lights, Landscape Garden, etc. that make it one of the most sought after neighborhoods.
PROP_IDPROPERTY_TYPESOCIETY_NAMECITYlocationBEDROOM_NUMBALCONY_NUMAREAPrice_per_sqftPRICEAGEFACINGFURNISHamenity_luxuryFLOOR_NUMLATITUDELONGITUDEPROP_NAMETOTAL_FLOORFORMATTED_LANDMARK_DETAILSDESCRIPTION
5140H71230840flatOn RequestKolkata NorthSodepur2.01.07153000.00.21Relatively New Property4Unfurnished246.0422.70201388.389120on request4.0['1 Religious Place', '13 Hospitals', '1 Attraction', '1 Bus Depot']On request is one of kolkata north's most sought after destination for apartments and this 2 bhk flat in sodepur is your opportunity to be a part of this community. Containing 2 bedroom(s), 2 bathrooms and 1 balcony, this flat is spread over a super built up area of 715 sq.Ft. This flat lies on the top level of a 4 storey building. This is a ready to move project and the property is 1-5 years old.\n Additional details :\n\nNo power backup is available.\nThe society has dedicated security guards for every tower.
5141Y71230782flatOn RequestKolkata NorthSodepur3.01.011153000.00.33Relatively New Property2Fully furnished246.0422.70201388.389120on request4.0['1 Religious Place', '13 Hospitals', '1 Attraction', '1 Bus Depot']This beautiful 3 bhk flat in sodepur, kolkata north is situated in on request, one of the popular residential society in kolkata north. Constructed on a super built up area of 1115 sq.Ft., the flat comprises 3 bedroom(s), 2 bathrooms and 1 balcony. This flat is situated on the top floor of this 4 floors tall residential building. This 1-5 years old property is available for immediate possession as the project is ready to move.\n Additional details :\n\nNo power backup is available.\nThe society has dedicated security guards for every tower.
5142B71230542flatOn RequestKolkata NorthSodepur3.01.011103000.00.33Relatively New Property7Unfurnished246.0422.70196088.389504on request4.0['1 Religious Place', '13 Hospitals', '1 Attraction', '1 Bus Depot']Situated in sodepur, kolkata north, on request is a well planned society that offers a pleasant living experience to its residents. This 3 bhk flat in kolkata north is your opportunity to be a part of this community. Containing 3 bedroom(s), 2 bathrooms and 1 balcony, this flat is spread over a super built up area of 1110 sq.Ft. This flat lies on the top level of a 4 storey building. Being a ready to move project, you can expect immediate possession of this 1-5 years old property.
5143W71230298flatOn RequestKolkata NorthSodepur2.01.07103000.00.21Relatively New Property8Fully furnished246.0422.70215988.389192on request4.0['1 Religious Place', '13 Hospitals', '1 Attraction', '1 Bus Depot']Check out this 2 bhk apartment for sale in on request, a popular residential project that houses in-Demand flats in sodepur, kolkata north. The flat occupies a super built up area of 710 sq.Ft. That consists of 2 bedrooms, 2 bathrooms and 1 balcony. This flat lies on the top level of a 4 storey building. This 1-5 years old property is available for immediate possession as the project is ready to move.
5144W71222584flatSiddha Eden LakevilleKolkata NorthBt Road2.01.011005954.00.66Old Property3Fully furnished673.01022.61266488.377414Siddha Eden LakeVille24.0['1 Metro Station', '1 Shopping', '1 Connectivity', '2 Educations', '1 Hospital', '1 Airport', '1 Railway Station']It is a spacious 2 bhk with 2 room, 2 bath, 1 balcony, east garden swimming pool facing flat on higher floor, 1 covered parking, ready to move property for immediate sell.
5145U71217472flatShyama ApartmentKolkata SouthBehala2.01.07304520.00.33Relatively New Property8Fully furnished307.0222.49780988.309352Shyama Apartment2.0['1 Shopping', '8 Religious Places', '4 ATMs', '18 Hospitals', '2 Pharmacys', '1 Bus Depot']There is no brokerage charges for this property\nProject name: Shyama apartment\nLocality: Behala 14 no.\nPossession: Ready to move\nFlat size (Sq. Ft.): 2 bhk (750) (720)\n2 bed room dinning & kitchen 2 toilets & balcony south east facing\nFloor: 2nd no lift\nPrice: Rs.4500/-\nParking: Not available .\nLoan :Available from all banks.\nThank you
5146E64737128flatPrivetKolkata SouthNew Alipore3.01.020009000.01.80Moderately Old7Luxury furnished192.0222.51435888.325146privet5.0['1 Shopping', '5 Religious Places', '5 ATMs', '21 Hospitals', '1 Pharmacy', '1 Bus Depot', '1 Miscellaneous']This beautiful 3 bhk flat in new alipore, kolkata south is situated in privet, one of the popular residential society in kolkata south. The flat occupies a super built up area of 2000 sq.Ft. That consists of 3 bedrooms, 3 bathrooms and 1 balcony. The property is located on the 2nd floor of a 5 floor tall building. An added advantage of this 10+ year(s) old flat is that it is available for immediate possession as the project is already ready to move.
5147J66826540flatAmbuja Upohar The CondovilleKolkata SouthChak Garia3.02.018438410.01.55New Property7Luxury furnished135.01422.48596688.396453Ambuja Upohar The Condoville19.0['1 Metro Station', '14 Religious Places', '3 ATMs', '13 Hospitals', '1 Pharmacy', '1 Bus Depot', '1 Miscellaneous', '1 Library']Ambuja upohar the condoville is one of the most popular destination for buying apartments/ flats in chak garia, kolkata south. You too can be a part of this society by purchasing this 3 bhk flat here. The floor plan additionally contains 3 bedroom(s), 2 bathrooms and 2 balconies. All in all, the flat is spread over a super built up area of 1843 sq.Ft. The flat has a total of 19 floors and this property is situated on 14th floor. Being a ready to move project, you can expect immediate possession of this 5-10 years old property.
5148E66826562flatAmbuja Upohar The CondovilleKolkata SouthChak Garia3.02.020798417.01.75New Property7Luxury furnished135.01022.48596688.396453Ambuja Upohar The Condoville19.0['1 Metro Station', '14 Religious Places', '3 ATMs', '13 Hospitals', '1 Pharmacy', '1 Bus Depot', '1 Miscellaneous', '1 Library']Looking for a 3 bhk property for sale in kolkata south? Buy this 3 bhk flat in ambuja upohar the condoville that is situated in chak garia, kolkata south. The floor plan additionally contains 3 bedroom(s), 3 bathrooms and 2 balconies. All in all, the flat is spread over a super built up area of 2079 sq.Ft. The property is located on the 10th floor of a 19 floors tall building. As the project is already ready to move, so you can easily move into this 5-10 years old property.
5149J71210104flatDlf New Town HeightsKolkata EastNew Town2.01.012575887.00.74New Property3Fully furnished706.0322.60069188.469454DLF New Town Heights17.0['2 Religious Places', '3 ATMs', '5 Hospitals', '4 Office Complexes', '1 Parking', '2 Bus Depots', '1 Miscellaneous']It is a standard 2 bhk with 2 room, 2 bath, 1 big balcony, east garden swimming pool facing flat on lower floor, 1 basement parking, ready to move property for immediate sell.